Piloting racial bias training for hospital emergency department providers treating patients with opioid use disorder.

Racial disparities in opioid overdose have increased in recent years. Several studies have linked these disparities to health care providers' inequitable delivery of opioid use disorder (OUD) services. In response, health care policymakers and systems have designed new programs to improve equitable OUD care delivery. Racial bias training has been 1 commonly utilized program. Racial bias training educates providers about the existence of racial disparities in the treatment of people who use drugs and the role of implicit bias. Our study evaluates a pilot racial bias training delivered to 25 hospital emergency providers treating patients with OUDs in 2 hospitals in Detroit, Michigan. We conducted a 3-part survey, including a baseline assessment, post-training assessment, and a 2-month follow-up to evaluate the acceptability and feasibility of scaling the racial bias training to larger audiences. We also investigate preliminary data on changes in self-awareness of implicit bias, knowledge of training content, and equity in care delivery to patients with OUD. Using qualitative survey response data, we found that training participants were satisfied with the content and quality of the training and especially valued the small-group discussions, motivational interviewing, and historical context.

Characteristics and engagement among English-language online forums for addiction recovery available in the US.

In developing public resources for the Networks Enhancing Addiction Recovery - Forum Activity Roadmap (NEAR-FAR), we completed a systematic observational study of English-language online forums related to recovery from alcohol or other drug addiction in late 2021. Among 207 identified forums, the majority were classified as "general addiction" or alcohol-focused, though classifications related to other substances were common on websites hosting multiple forums. Commonly used social media platforms such as Reddit, Facebook, or Quora offered easily accessible venues for individuals seeking online support related to a variety of substances. Forums were related to established recovery programs such as 12-step and SMART Recovery as well as other nonprofit and for-profit recovery programs, and to community forums without formal recovery programming. Among 148 forums with any observed user activity, the median time between unique user engagements was 27 days (inter-quartile range: 2-74). Among 98 forums with past-month posting activity, we found a median of <10 posts per week (inter-quartile range: 1-78). This study compares three metrics of observed forum activity (posts per week, responses per post, time between unique user engagements) and operationalizes forum characteristics that may potentiate opportunities for enhanced engagement and social support in addiction recovery.

Achieving the Potential of Naloxone Saturation by Measuring Distribution.

This Viewpoint advocates for improved strategies to measure naloxone distribution and evaluate how effectively naloxone reaches people most likely to experience or witness an opioid overdose.

Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach.

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877-0.892 vs. C-statistic = 0.871; 95%CI = 0.863-0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.

Development of a Synthetic Population Model for Assessing Excess Risk for Cardiovascular Disease Death.

Importance: Evaluating the association of social determinants of health with chronic diseases at the population level requires access to individual-level factors associated with disease, which are rarely available for large populations. Synthetic populations are a possible alternative for this purpose. Objective: To construct and validate a synthetic population that statistically mimics the characteristics and spatial disease distribution of a real population, using real and synthetic data. Design, Setting, and Participants: This population-based decision analytical model used data for Allegheny County, Pennsylvania, collected from January 2015 to December 2016, to build a semisynthetic population based on the synthetic population used by the modeling and simulation platform FRED (A Framework for Reconstructing Epidemiological Dynamics). Disease status was assigned to this population using health insurer claims data from the 3 major insurance providers in the county or from the National Health and Nutrition Examination Survey. Biological, social, and other variables were also obtained from the National Health Interview Survey, Allegheny County, and public databases. Data analysis was performed from November 2016 to February 2020. Exposures: Risk of cardiovascular disease (CVD) death. Main Outcomes and Measures: Difference between expected and observed CVD death risk. A validated risk equation was used to estimate CVD death risk. Results: The synthetic population comprised 1 188 112 individuals with demographic characteristics similar to those of the 2010 census population in the same county. In the synthetic population, the mean (SD) age was 40.6 (23.3) years, and 622 997 were female individuals (52.4%). Mean (SD) observed 4-year rate of excess CVD death risk at the census tract level was -40 (523) per 100 000 persons. The correlation of social determinant data with difference between expected and observed CVD death risk indicated that income- and education-based social determinants were associated with risk. Estimating improved social determinants of health and biological factors associated with disease did not entirely remove the excess in CVD death rates. That is, a 20% improvement in the most significant determinants still resulted in 105 census tracts with excess CVD death risk, which represented 24% of the county population. Conclusions and Relevance: The results of this study suggest that creating a geographically explicit synthetic population from real and synthetic data is feasible and that synthetic populations are useful for modeling disease in large populations and for estimating the outcome of interventions.

Calculating the incidence rate of sibling bereavement among children and adolescents across the United States: A proposed method.

This study proposes a method for calculating the annual incidence rate of sibling bereavement among US youth using national epidemiological data. The proposed model combines data on family household size with national death statistics to calculate the number of siblings affected by the death of a child annually. From 2012 to 2015, an average of 61,389 children per year experienced the death of a sibling, resulting in an estimate of 0.0832% of children bereaved by the death of a sibling annually. Data indicate a need for greater awareness and dialog concerning the frequency with which children experience the death of a sibling.

Linking Opioid-Overdose Data to Human Services and Criminal Justice Data: Opportunities for Intervention.

OBJECTIVES: In Allegheny County, Pennsylvania, the incidence of opioid-related overdose deaths increased from 17.4 per 100 000 population in 2008 to 23.9 per 100 000 population in 2014. Our objectives were to describe local demographic characteristics of this epidemic, identify public human services targets for intervention, determine temporal relationships between use of public human services and overdose mortality, and provide recommendations about potentially beneficial interventions. METHODS: We used autopsy data from the Allegheny County Medical Examiner to link people who died of overdoses from 2008 through 2014 to their premortem incarcerations and use of mental health services and substance use disorder services. We calculated the frequency of use of public human services by decedents and the interval between the last use of these services and overdose death. RESULTS: Of the 1399 decedents, 957 (68.4%) had a public human service encounter before overdose death. Of these 957 decedents, 531 (55.5%) had ever been incarcerated, 616 (64.4%) had ever used a mental health service, and 702 (73.4%) had ever used a substance use disorder service. Of 211 decedents incarcerated in the year before their overdose death, 54 (25.6%) overdosed within 30 days of their last release from jail. Of 510 decedents using mental health services in the year before death, 231 (45.3%) overdosed within 30 days of their last use of the services. Of 350 decedents using substance use disorder services in the year before their overdose death, 134 (38.3%) overdosed within 30 days of their last use of the services. CONCLUSIONS: Merging data on overdose mortality with data on use of public human services can be a useful strategy to identify trends in, and factors contributing to, the opioid epidemic; to target interventions; and to stimulate collaboration to address the epidemic.

Characteristics of an overdose prevention, response, and naloxone distribution program in Pittsburgh and Allegheny County, Pennsylvania.

Prevention Point Pittsburgh (PPP) is a public health advocacy organization that operates Allegheny County's only needle exchange program. In 2002, PPP implemented an Overdose Prevention Program (OPP) in response to an increase in heroin-related and opioid-related overdose fatalities in the region. In 2005, the OPP augmented overdose prevention and response trainings to include naloxone training and prescription. The objective of our study is to describe the experiences of 426 individuals who participated in the OPP between July 1, 2005, and December 31, 2008. Of these, 89 individuals reported administering naloxone in response to an overdose in a total of 249 separate overdose episodes. Of these 249 overdose episodes in which naloxone was administered, participants reported 96% were reversed. The data support findings from a growing body of research on similar programs in other cities. Community-based OPPs that equip drug users with skills to identify and respond to an overdose and prescribe naloxone can help users and their peers prevent and reverse potentially fatal overdoses without significant adverse consequences.

Partnerships and pathways of dissemination: the National Institute on Drug Abuse-Substance Abuse and Mental Health Services Administration Blending Initiative in the Clinical Trials Network.

Since 2001, the National Drug Abuse Treatment Clinical Trials Network (CTN) has worked to put the results of its trials into the hands of community treatment programs, in large part through its participation in the National Institute on Drug Abuse-Substance Abuse and Mental Health Services Administration Blending Initiative and its close involvement with the Center for Substance Abuse Treatment's Addiction Technology Transfer Centers. This article describes (a) the CTN's integral role in the Blending Initiative, (b) key partnerships and dissemination pathways through which the results of CTN trials are developed into blending products and then transferred to community treatment programs, and (c) three blending initiatives involving buprenorphine, motivational incentives, and motivational interviewing. The Blending Initiative has resulted in high utilization of its products, preparation of more than 200 regional trainers, widespread training of service providers in most U.S. States, Puerto Rico, and the U.S. Virgin Islands and movement toward the development of Web-based implementation supports and technical assistance. Implications for future directions of the Blending Initiative and opportunities for research are discussed.

Perspectives on a community-based course for public health students.

In recent years, there has been an increase in collaborations between universities and local community agencies. During the 2005-2006 academic year, the Department of Behavioral and Community Health Sciences at the University of Pittsburgh Graduate School of Public Health began a community-university partnership with Hosanna House Inc., a local community service center. The initial phase of this partnership included holding a departmental community development course at the community service center. Information from student journals, course evaluations, and key informant interviews were used to describe the attitudes and perspectives of the students, course instructor, and key informants toward this experience. Overall, this experience positively affected the personal and professional development of the students and was well received by faculty and staff within the academic department and community service center. The authors anticipate that this information will promote and serve as a reference for similar community-university partnerships at other schools of public health.